import { describe, expect, it, vi } from 'vitest';
import {
  createEmbeddingClientFromEnvironment,
  embeddingEndpoint,
  EmbeddingClientError,
  OpenAiCompatibleEmbeddingClient
} from '../../server/regulatory/embedding-client';

describe('regulatory embedding client', () => {
  it('allows loopback HTTP, requires remote HTTPS, and requires explicit dimensions', () => {
    expect(embeddingEndpoint('https://litellm.work.example/v1').href).toBe('https://litellm.work.example/v1/embeddings');
    expect(embeddingEndpoint('http://127.0.0.1:8080/v1').href).toBe('http://127.0.0.1:8080/v1/embeddings');
    expect(() => embeddingEndpoint('http://litellm.work.example/v1')).toThrow(/HTTPS/);
    expect(() => createEmbeddingClientFromEnvironment({
      ASSESSOR_RAG_EMBEDDING_BASE_URL: 'https://litellm.work.example/v1',
      ASSESSOR_RAG_EMBEDDING_MODEL: 'multilingual-model',
      ASSESSOR_RAG_EMBEDDING_API_KEY: 'secret'
    })).toThrow(/DIMENSIONS is required/);
  });

  it('requests a shortened dimension only when explicitly configured', async () => {
    const bodies: unknown[] = [];
    const fetcher = vi.fn(async (_url: string | URL | Request, init?: RequestInit) => {
      bodies.push(JSON.parse(String(init?.body)));
      return new Response(JSON.stringify({ data: [{ index: 0, embedding: Array(64).fill(0) }] }), { status: 200 });
    });
    const client = createEmbeddingClientFromEnvironment({
      ASSESSOR_RAG_EMBEDDING_BASE_URL: 'https://litellm.work.example/v1',
      ASSESSOR_RAG_EMBEDDING_MODEL: 'text-embedding-3-large',
      ASSESSOR_RAG_EMBEDDING_API_KEY: 'secret',
      ASSESSOR_RAG_EMBEDDING_DIMENSIONS: '64',
      ASSESSOR_RAG_EMBEDDING_REQUEST_DIMENSIONS: 'true'
    }, fetcher);

    await client.embed(['Synthetic text']);

    expect(bodies).toEqual([{ model: 'text-embedding-3-large', input: ['Synthetic text'], encoding_format: 'float', dimensions: 64 }]);
  });

  it('returns embeddings in provider index order without exposing the API key', async () => {
    const fetcher = vi.fn(async (_url: string | URL | Request, init?: RequestInit) => {
      expect(init?.redirect).toBe('error');
      expect(new Headers(init?.headers).get('authorization')).toBe('Bearer secret');
      expect(JSON.parse(String(init?.body))).toEqual({ model: 'multilingual-model', input: ['Deutsch', 'English'], encoding_format: 'float' });
      return new Response(JSON.stringify({ data: [
        { index: 1, embedding: [0, 1] },
        { index: 0, embedding: [1, 0] }
      ] }), { status: 200 });
    });
    const client = new OpenAiCompatibleEmbeddingClient({
      endpoint: new URL('https://litellm.work.example/v1/embeddings'),
      model: 'multilingual-model', apiKey: 'secret', dimensions: 2, timeoutMs: 1_000, fetcher
    });

    await expect(client.embed(['Deutsch', 'English'])).resolves.toEqual([[1, 0], [0, 1]]);
    expect(fetcher).toHaveBeenCalledOnce();
  });

  it('rejects incompatible vector dimensions', async () => {
    const client = new OpenAiCompatibleEmbeddingClient({
      endpoint: new URL('https://litellm.work.example/v1/embeddings'),
      model: 'multilingual-model', apiKey: 'secret', dimensions: 3, timeoutMs: 1_000,
      fetcher: async () => new Response(JSON.stringify({ data: [{ index: 0, embedding: [1, 0] }] }), { status: 200 })
    });
    await expect(client.embed(['query'])).rejects.toMatchObject({ code: 'embedding_invalid_dimensions' } satisfies Partial<EmbeddingClientError>);
  });
});